Intelligent Connectomic Analysis Tool for Dense Neuronal Circuits Project Summary: The lack of basic understanding of neuronal functions and disease processes is a big factor of failures in creating drugs for neurological diseases. High-resolution maps of the complex connectivity of neuronal circuits correlating with functional and/or molecular markers offer invaluable insights into the functional organization of the neuronal structures, which is a key to understanding the brain in health and disease. There is a strong interest in elucidating and quantifying the connectomics of brain networks with subcellular resolution using electron microscopy (EM) and correlate with functional fluorescence microscopy data. The ultimate goal is to elucidate human brain functions and the mechanisms of human brain disorders. This is critically important to enable new diagnostics and therapies for brain disorders. The reconstruction and analyses of neuronal networks is challenging in part due to the joint requirement of large volume and high resolution and a large gap in connectomic analysis solutions. There is a strong need for next generation, well supported, integrated, easy to use and highly automated analysis tools to detect and classify neurons, trace arbor branches, identify synapses, spines and synaptic vesicles that increase the throughput of otherwise prohibitively time-consuming analyses in connectomic experiments. There is also a strong need for tools to perform downstream data-driven analysis such as functional inference from structure and phenotypic discovery. Powered by machine learning and DRVision innovations and collaborating with Dr. Rachel Wong and 9 additional labs, this project proposes to create an intelligent connectomic analysis (ICA) tool optimized for dense neuronal circuits. The tool will be commercially supported and integrated with DRVision?s flagship product Aivia to (1) provide accurate and automated neuron tracing in 3D EM and 3D fluorescence data up to multi-terabytes, (2) identify pre- and post-synaptic dendrite segments, (3) correlate light and electron microscopy data, quantify and classify neurons and sub-cellular components, (4) extract and analyze neuron circuits, (5) provide tools for phenotype discoveries, (6) seamlessly integrate the pipeline of ground truth (GT) annotation, editing, and machine learning workflow, and (7) access the required computing infrastructure, database connection, and exchange of data with other tools.